Generator outputs of InfoGAN

Similar to all previous GANs that have been presented to us, we've trained InfoGAN for 40,000 steps. After the training is completed, we're able to run the InfoGAN generator to generate new outputs using the model saved on the infogan_mnist.h5 file. The following validations are conducted:

1. Generate digits 0 to 9 by varying the discrete labels from 0 to 9. Both continuous codes are set to zero. The results are shown in Figure 6.1.5. We can see that the InfoGAN discrete code can control the digits produced by the generator:

   python3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5 --digit=0 --code1=0 --code2=0

   to

   python3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5 --digit=9 --code1=0 --code2=0
   

2. Examine the effect of the first continuous code to understand which attribute has been affected. We vary the first continuous code from -2.0 to 2.0 for digits 0 to 9. The second continuous code is set to 0.0. Figure 6.1.6 shows that the first continuous...